Studying Characteristics of Huay Saneng and Predicting The Water Levels by Comparison with Other Regression Models
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Abstract
The drought problem in Surin Province has become complex issue over the course of the last five years. Water level in reservoir is directly associated with drought. Therefore, understanding the root cause of the problem is of utmost importance. This research investigated which attributes were subject to drought in a reservoir called Huay Saneng and which machine learning regression models showed the best accuracy using data from the last 5 years; with 1,825 records derived from Royal Irrigation Department operation on Huay Saneng area. The research finding established that Decision tree regression showed satisfactory results with 94.7% reliability; better than Multiple linear regression, Polynomial regression, and Random forest regression. The use of four attributes consisting of date, evaporation, water levels and rain can assist to make prediction with satisfactory results. In addition, the research found that the levels of evaporation of water is an important factor in assisting prediction through the process of feature selection. The research discussed how conservation of water levels in the past five years tended to be less inclined and how future research may be improved in order to predict drought accurately in the future.
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